Identification of interesting association rules through objective and subjective measures analysis / Identificação de regras de associação interessantes por meio de análises com medidas objetivas e subjetivas
AUTOR(ES)
Roberta Akemi Sinoara
DATA DE PUBLICAÇÃO
2006
RESUMO
Association is a data mining task which has been applied in several real problems. However, due to the huge number of association rules that can be generated, it is hard for users to identify interesting knowledge. To assist users in finding interesting rules, evaluation measures can be used. Those measures are usually divided into objective and subjective. Objective measures are more general, but they can be insufficient because they do not consider user s and domain s features. On the other hand, getting users s knowledge and interest needed to calculate subjective measures can be a difficult task. In this context, a methodology to identify interesting association rules is proposed in this work. This methodology combines analysis with objective and subjective measures, aiming to use the advantages of each kind of measure and to make user s participation easier. Objective measures are used to select some potentially interesting rules for the user s evaluation. These rules and the evaluation are used to calculate subjective measures. Then, the subjective measures are used to assist the user in identifying interesting rules according to the knowledge obtained during the evaluation. To make the methodology use practicable, a computational module, named RulEE-SEAR, was developed to explore the association rules with subjective measures. Using this module and other existing tools, a case study was done. A urban life quality database was used and a specialist in this area participated in the interesting association rules identification. That case study showed that the methodology proposed is feasible.
ASSUNTO(S)
medidas de avaliação data mining regras de associação evaluation measures association rules mineração de dados
